Connor Tan is a physicist and senior data scientist working for a multinational energy company where he co-founded and leads a data science team. He holds a first-class degree in experimental and theoretical physics from Cambridge university. With a master's in particle astrophysics. He specializes in the application of machine learning models and Bayesian methods. Today we explore the history, pratical utility, and unique capabilities of Bayesian methods. We also discuss the computational difficulties inherent in Bayesian methods along with modern methods for approximate solutions such as Markov Chain Monte Carlo. Finally, we discuss how Bayesian optimization in the context of automl may one day put Data Scientists like Connor out of work.

Panel: Dr. Keith Duggar, Alex Stenlake, Dr. Tim Scarfe

00:00:00 Duggars philisophical ramblings on Bayesianism

00:05:10 Introduction

00:07:30 small datasets and prior scientific knowledge

00:10:37 Bayesian methods are probability theory

00:14:00 Bayesian methods demand hard computations

00:15:46 uncertainty can matter more than estimators

00:19:29 updating or combining knowledge is a key feature

00:25:39 Frequency or Reasonable Expectation as the Primary Concept 

00:30:02 Gambling and coin flips

00:37:32 Rev. Thomas Bayes's pool table

00:40:37 ignorance priors are beautiful yet hard

00:43:49 connections between common distributions

00:49:13 A curious Universe, Benford's Law

00:55:17 choosing priors, a tale of two factories

01:02:19 integration, the computational Achilles heel

01:35:25 Bayesian social context in the ML community

01:10:24 frequentist methods as a first approximation

01:13:13 driven to Bayesian methods by small sample size

01:18:46 Bayesian optimization with automl, a job killer?

01:25:28 different approaches to hyper-parameter optimization

01:30:18 advice for aspiring Bayesians

01:33:59 who would connor interview next?

Connor Tann: https://www.linkedin.com/in/connor-tann-a92906a1/

https://twitter.com/connossor